Overview

Dataset statistics

Number of variables23
Number of observations1687861
Missing cells100915
Missing cells (%)0.3%
Duplicate rows168735
Duplicate rows (%)10.0%
Total size in memory296.2 MiB
Average record size in memory184.0 B

Variable types

Unsupported1
Numeric15
Boolean7

Alerts

Dataset has 168735 (10.0%) duplicate rowsDuplicates
national_inv is highly correlated with sales_6_month and 2 other fieldsHigh correlation
in_transit_qty is highly correlated with forecast_3_month and 7 other fieldsHigh correlation
forecast_3_month is highly correlated with in_transit_qty and 8 other fieldsHigh correlation
forecast_6_month is highly correlated with in_transit_qty and 8 other fieldsHigh correlation
forecast_9_month is highly correlated with in_transit_qty and 8 other fieldsHigh correlation
sales_1_month is highly correlated with in_transit_qty and 8 other fieldsHigh correlation
sales_3_month is highly correlated with in_transit_qty and 7 other fieldsHigh correlation
sales_6_month is highly correlated with national_inv and 9 other fieldsHigh correlation
sales_9_month is highly correlated with national_inv and 8 other fieldsHigh correlation
min_bank is highly correlated with national_inv and 8 other fieldsHigh correlation
perf_6_month_avg is highly correlated with perf_12_month_avgHigh correlation
perf_12_month_avg is highly correlated with perf_6_month_avgHigh correlation
pieces_past_due is highly correlated with forecast_3_month and 4 other fieldsHigh correlation
lead_time has 100894 (6.0%) missing values Missing
national_inv is highly skewed (γ1 = 340.2858003) Skewed
in_transit_qty is highly skewed (γ1 = 166.1834042) Skewed
forecast_3_month is highly skewed (γ1 = 138.9683252) Skewed
forecast_6_month is highly skewed (γ1 = 138.9614272) Skewed
forecast_9_month is highly skewed (γ1 = 143.2988747) Skewed
sales_1_month is highly skewed (γ1 = 196.1199899) Skewed
sales_3_month is highly skewed (γ1 = 141.2863795) Skewed
sales_6_month is highly skewed (γ1 = 139.176712) Skewed
sales_9_month is highly skewed (γ1 = 135.0541915) Skewed
min_bank is highly skewed (γ1 = 131.2126489) Skewed
pieces_past_due is highly skewed (γ1 = 412.3919004) Skewed
local_bo_qty is highly skewed (γ1 = 165.1905479) Skewed
sku is an unsupported type, check if it needs cleaning or further analysis Unsupported
national_inv has 108425 (6.4%) zeros Zeros
in_transit_qty has 1344662 (79.7%) zeros Zeros
forecast_3_month has 1177722 (69.8%) zeros Zeros
forecast_6_month has 1084111 (64.2%) zeros Zeros
forecast_9_month has 1033241 (61.2%) zeros Zeros
sales_1_month has 959817 (56.9%) zeros Zeros
sales_3_month has 759225 (45.0%) zeros Zeros
sales_6_month has 647038 (38.3%) zeros Zeros
sales_9_month has 585994 (34.7%) zeros Zeros
min_bank has 872331 (51.7%) zeros Zeros
pieces_past_due has 1662571 (98.5%) zeros Zeros
perf_6_month_avg has 39013 (2.3%) zeros Zeros
perf_12_month_avg has 32975 (2.0%) zeros Zeros
local_bo_qty has 1664518 (98.6%) zeros Zeros

Reproduction

Analysis started2022-10-23 19:21:51.456137
Analysis finished2022-10-23 19:25:05.336865
Duration3 minutes and 13.88 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

sku
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size12.9 MiB

national_inv
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct14969
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean496.1117818
Minimum-27256
Maximum12334404
Zeros108425
Zeros (%)6.4%
Negative5888
Negative (%)0.3%
Memory size12.9 MiB
2022-10-23T20:25:05.379516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-27256
5-th percentile0
Q14
median15
Q380
95-th percentile922
Maximum12334404
Range12361660
Interquartile range (IQR)76

Descriptive statistics

Standard deviation29615.23383
Coefficient of variation (CV)59.69467954
Kurtosis131276.5926
Mean496.1117818
Median Absolute Deviation (MAD)13
Skewness340.2858003
Sum837367232
Variance877062074.9
MonotonicityNot monotonic
2022-10-23T20:25:05.434685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0108425
 
6.4%
2107869
 
6.4%
390577
 
5.4%
469588
 
4.1%
558916
 
3.5%
158829
 
3.5%
650832
 
3.0%
746399
 
2.7%
1046277
 
2.7%
840147
 
2.4%
Other values (14959)1010001
59.8%
ValueCountFrequency (%)
-272561
< 0.1%
-254142
< 0.1%
-221542
< 0.1%
-176981
< 0.1%
-176691
< 0.1%
-134911
< 0.1%
-99251
< 0.1%
-82301
< 0.1%
-81701
< 0.1%
-81301
< 0.1%
ValueCountFrequency (%)
123344041
< 0.1%
123244561
< 0.1%
123150721
< 0.1%
123090961
< 0.1%
122851001
< 0.1%
121816121
< 0.1%
121664401
< 0.1%
63632761
< 0.1%
63529321
< 0.1%
63353001
< 0.1%

lead_time
Real number (ℝ≥0)

MISSING

Distinct32
Distinct (%)< 0.1%
Missing100894
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean7.872267035
Minimum0
Maximum52
Zeros10511
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:05.597215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median8
Q39
95-th percentile12
Maximum52
Range52
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.056023998
Coefficient of variation (CV)0.896314107
Kurtosis26.2372275
Mean7.872267035
Median Absolute Deviation (MAD)1
Skewness4.556295428
Sum12493028
Variance49.78747466
MonotonicityNot monotonic
2022-10-23T20:25:05.640276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
8682186
40.4%
2337402
20.0%
12199700
 
11.8%
4128537
 
7.6%
9123649
 
7.3%
5230113
 
1.8%
316253
 
1.0%
1014192
 
0.8%
010511
 
0.6%
1410314
 
0.6%
Other values (22)34110
 
2.0%
(Missing)100894
 
6.0%
ValueCountFrequency (%)
010511
 
0.6%
121
 
< 0.1%
2337402
20.0%
316253
 
1.0%
4128537
 
7.6%
54031
 
0.2%
65365
 
0.3%
7209
 
< 0.1%
8682186
40.4%
9123649
 
7.3%
ValueCountFrequency (%)
5230113
1.8%
4048
 
< 0.1%
3535
 
< 0.1%
30312
 
< 0.1%
2884
 
< 0.1%
26105
 
< 0.1%
257
 
< 0.1%
24115
 
< 0.1%
2314
 
< 0.1%
22133
 
< 0.1%

in_transit_qty
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct5230
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean44.05202209
Minimum0
Maximum489408
Zeros1344662
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:05.687695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile68
Maximum489408
Range489408
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1342.741731
Coefficient of variation (CV)30.48081943
Kurtosis39606.10405
Mean44.05202209
Median Absolute Deviation (MAD)0
Skewness166.1834042
Sum74353646
Variance1802955.355
MonotonicityNot monotonic
2022-10-23T20:25:05.737677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01344662
79.7%
136515
 
2.2%
222236
 
1.3%
317189
 
1.0%
415364
 
0.9%
513003
 
0.8%
611384
 
0.7%
108964
 
0.5%
88822
 
0.5%
78131
 
0.5%
Other values (5220)201590
 
11.9%
ValueCountFrequency (%)
01344662
79.7%
136515
 
2.2%
222236
 
1.3%
317189
 
1.0%
415364
 
0.9%
513003
 
0.8%
611384
 
0.7%
78131
 
0.5%
88822
 
0.5%
96285
 
0.4%
ValueCountFrequency (%)
4894081
< 0.1%
4876801
< 0.1%
3280601
< 0.1%
3271561
< 0.1%
2920931
< 0.1%
2889601
< 0.1%
2887681
< 0.1%
2853651
< 0.1%
2767031
< 0.1%
2546881
< 0.1%

forecast_3_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct7825
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean178.1192836
Minimum0
Maximum1427612
Zeros1177722
Zeros (%)69.8%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:05.786830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile300
Maximum1427612
Range1427612
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5026.553102
Coefficient of variation (CV)28.22015113
Kurtosis25637.5503
Mean178.1192836
Median Absolute Deviation (MAD)0
Skewness138.9683252
Sum300640414
Variance25266236.09
MonotonicityNot monotonic
2022-10-23T20:25:05.834555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01177722
69.8%
131452
 
1.9%
226612
 
1.6%
519740
 
1.2%
418563
 
1.1%
316194
 
1.0%
1014370
 
0.9%
613612
 
0.8%
1211031
 
0.7%
2010946
 
0.6%
Other values (7815)347618
 
20.6%
ValueCountFrequency (%)
01177722
69.8%
131452
 
1.9%
226612
 
1.6%
316194
 
1.0%
418563
 
1.1%
519740
 
1.2%
613612
 
0.8%
78152
 
0.5%
810357
 
0.6%
96880
 
0.4%
ValueCountFrequency (%)
14276121
< 0.1%
12183282
< 0.1%
11266561
< 0.1%
11030841
< 0.1%
10583961
< 0.1%
10465921
< 0.1%
10372281
< 0.1%
10137081
< 0.1%
9925401
< 0.1%
9807801
< 0.1%

forecast_6_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct11114
Distinct (%)0.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean344.9866636
Minimum0
Maximum2461360
Zeros1084111
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:05.885719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile600
Maximum2461360
Range2461360
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9795.151861
Coefficient of variation (CV)28.39284209
Kurtosis25189.90379
Mean344.9866636
Median Absolute Deviation (MAD)0
Skewness138.9614272
Sum582289190
Variance95944999.99
MonotonicityNot monotonic
2022-10-23T20:25:05.936967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01084111
64.2%
127857
 
1.7%
226492
 
1.6%
319975
 
1.2%
418782
 
1.1%
516649
 
1.0%
616113
 
1.0%
1015810
 
0.9%
812976
 
0.8%
2011981
 
0.7%
Other values (11104)437114
25.9%
ValueCountFrequency (%)
01084111
64.2%
127857
 
1.7%
226492
 
1.6%
319975
 
1.2%
418782
 
1.1%
516649
 
1.0%
616113
 
1.0%
711512
 
0.7%
812976
 
0.8%
96761
 
0.4%
ValueCountFrequency (%)
24613601
< 0.1%
24460721
< 0.1%
21191481
< 0.1%
21097401
< 0.1%
21041281
< 0.1%
20943361
< 0.1%
20873961
< 0.1%
20850441
< 0.1%
20568201
< 0.1%
20368281
< 0.1%

forecast_9_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct13662
Distinct (%)0.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean506.3644307
Minimum0
Maximum3777304
Zeros1033241
Zeros (%)61.2%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:05.988241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile896
Maximum3777304
Range3777304
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14378.92356
Coefficient of variation (CV)28.39639337
Kurtosis27048.45231
Mean506.3644307
Median Absolute Deviation (MAD)0
Skewness143.2988747
Sum854672268
Variance206753442.8
MonotonicityNot monotonic
2022-10-23T20:25:06.037327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01033241
61.2%
126781
 
1.6%
225662
 
1.5%
320716
 
1.2%
419322
 
1.1%
517495
 
1.0%
616611
 
1.0%
1016153
 
1.0%
813953
 
0.8%
1212425
 
0.7%
Other values (13652)485501
28.8%
ValueCountFrequency (%)
01033241
61.2%
126781
 
1.6%
225662
 
1.5%
320716
 
1.2%
419322
 
1.1%
517495
 
1.0%
616611
 
1.0%
710402
 
0.6%
813953
 
0.8%
98012
 
0.5%
ValueCountFrequency (%)
37773041
< 0.1%
37608401
< 0.1%
32328201
< 0.1%
32292921
< 0.1%
32069481
< 0.1%
31963641
< 0.1%
31587321
< 0.1%
31493241
< 0.1%
31034601
< 0.1%
30620161
< 0.1%

sales_1_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct5764
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean55.92606851
Minimum0
Maximum741774
Zeros959817
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:06.089179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile107
Maximum741774
Range741774
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1928.195879
Coefficient of variation (CV)34.47758675
Kurtosis53855.92556
Mean55.92606851
Median Absolute Deviation (MAD)0
Skewness196.1199899
Sum94395374
Variance3717939.347
MonotonicityNot monotonic
2022-10-23T20:25:06.137072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0959817
56.9%
1144881
 
8.6%
278759
 
4.7%
349410
 
2.9%
437905
 
2.2%
529720
 
1.8%
623695
 
1.4%
718997
 
1.1%
817003
 
1.0%
1014605
 
0.9%
Other values (5754)313068
 
18.5%
ValueCountFrequency (%)
0959817
56.9%
1144881
 
8.6%
278759
 
4.7%
349410
 
2.9%
437905
 
2.2%
529720
 
1.8%
623695
 
1.4%
718997
 
1.1%
817003
 
1.0%
913983
 
0.8%
ValueCountFrequency (%)
7417741
< 0.1%
7417621
< 0.1%
7417501
< 0.1%
3936651
< 0.1%
3760251
< 0.1%
3694251
< 0.1%
3661911
< 0.1%
3618031
< 0.1%
3612391
< 0.1%
3595051
< 0.1%

sales_3_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct10495
Distinct (%)0.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean175.0259305
Minimum0
Maximum1105478
Zeros759225
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:06.189637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q315
95-th percentile350
Maximum1105478
Range1105478
Interquartile range (IQR)15

Descriptive statistics

Standard deviation5192.377625
Coefficient of variation (CV)29.6663335
Kurtosis24198.86065
Mean175.0259305
Median Absolute Deviation (MAD)1
Skewness141.2863795
Sum295419267
Variance26960785.4
MonotonicityNot monotonic
2022-10-23T20:25:06.238171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0759225
45.0%
1136966
 
8.1%
281289
 
4.8%
353082
 
3.1%
441533
 
2.5%
534128
 
2.0%
627829
 
1.6%
723243
 
1.4%
820904
 
1.2%
1017873
 
1.1%
Other values (10485)491788
29.1%
ValueCountFrequency (%)
0759225
45.0%
1136966
 
8.1%
281289
 
4.8%
353082
 
3.1%
441533
 
2.5%
534128
 
2.0%
627829
 
1.6%
723243
 
1.4%
820904
 
1.2%
917571
 
1.0%
ValueCountFrequency (%)
11054781
< 0.1%
11041811
< 0.1%
11005231
< 0.1%
10941121
< 0.1%
10912811
< 0.1%
10849741
< 0.1%
10815601
< 0.1%
10766231
< 0.1%
10706231
< 0.1%
10629791
< 0.1%

sales_6_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct14818
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean341.7288395
Minimum0
Maximum2146625
Zeros647038
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:06.289156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q331
95-th percentile696
Maximum2146625
Range2146625
Interquartile range (IQR)31

Descriptive statistics

Standard deviation9613.167104
Coefficient of variation (CV)28.13097987
Kurtosis24305.44501
Mean341.7288395
Median Absolute Deviation (MAD)2
Skewness139.176712
Sum576790439
Variance92412981.77
MonotonicityNot monotonic
2022-10-23T20:25:06.337160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0647038
38.3%
1127076
 
7.5%
279418
 
4.7%
352987
 
3.1%
441716
 
2.5%
534108
 
2.0%
628319
 
1.7%
723244
 
1.4%
821102
 
1.3%
918383
 
1.1%
Other values (14808)614469
36.4%
ValueCountFrequency (%)
0647038
38.3%
1127076
 
7.5%
279418
 
4.7%
352987
 
3.1%
441716
 
2.5%
534108
 
2.0%
628319
 
1.7%
723244
 
1.4%
821102
 
1.3%
918383
 
1.1%
ValueCountFrequency (%)
21466251
< 0.1%
21457151
< 0.1%
21335571
< 0.1%
21239461
< 0.1%
21178031
< 0.1%
21139011
< 0.1%
21122311
< 0.1%
20988521
< 0.1%
20865311
< 0.1%
17990991
< 0.1%

sales_9_month
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct18341
Distinct (%)1.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean525.2697007
Minimum0
Maximum3205172
Zeros585994
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:06.387195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q347
95-th percentile1060
Maximum3205172
Range3205172
Interquartile range (IQR)47

Descriptive statistics

Standard deviation14838.61352
Coefficient of variation (CV)28.24951354
Kurtosis22844.80575
Mean525.2697007
Median Absolute Deviation (MAD)4
Skewness135.0541915
Sum886581717
Variance220184451.3
MonotonicityNot monotonic
2022-10-23T20:25:06.436103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0585994
34.7%
1120294
 
7.1%
276521
 
4.5%
352294
 
3.1%
441399
 
2.5%
534323
 
2.0%
628784
 
1.7%
723545
 
1.4%
821115
 
1.3%
918744
 
1.1%
Other values (18331)684847
40.6%
ValueCountFrequency (%)
0585994
34.7%
1120294
 
7.1%
276521
 
4.5%
352294
 
3.1%
441399
 
2.5%
534323
 
2.0%
628784
 
1.7%
723545
 
1.4%
821115
 
1.3%
918744
 
1.1%
ValueCountFrequency (%)
32051721
< 0.1%
32049291
< 0.1%
32010351
< 0.1%
31973381
< 0.1%
31821481
< 0.1%
31673941
< 0.1%
31637941
< 0.1%
31208751
< 0.1%
31194501
< 0.1%
27581031
< 0.1%

min_bank
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct5568
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean52.77230339
Minimum0
Maximum313319
Zeros872331
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:06.488259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile125
Maximum313319
Range313319
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1254.983089
Coefficient of variation (CV)23.78109364
Kurtosis23549.24009
Mean52.77230339
Median Absolute Deviation (MAD)0
Skewness131.2126489
Sum89072260
Variance1574982.553
MonotonicityNot monotonic
2022-10-23T20:25:06.536555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0872331
51.7%
1252498
 
15.0%
2113010
 
6.7%
336035
 
2.1%
422180
 
1.3%
515929
 
0.9%
612059
 
0.7%
79574
 
0.6%
87841
 
0.5%
157375
 
0.4%
Other values (5558)339028
 
20.1%
ValueCountFrequency (%)
0872331
51.7%
1252498
 
15.0%
2113010
 
6.7%
336035
 
2.1%
422180
 
1.3%
515929
 
0.9%
612059
 
0.7%
79574
 
0.6%
87841
 
0.5%
96083
 
0.4%
ValueCountFrequency (%)
3133191
< 0.1%
3114231
< 0.1%
3104271
< 0.1%
3096671
< 0.1%
3080551
< 0.1%
3076271
< 0.1%
2910591
< 0.1%
2057861
< 0.1%
2048031
< 0.1%
2037341
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1686953 
True
 
907
(Missing)
 
1
ValueCountFrequency (%)
False1686953
99.9%
True907
 
0.1%
(Missing)1
 
< 0.1%
2022-10-23T20:25:06.686847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pieces_past_due
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct826
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.043724006
Minimum0
Maximum146496
Zeros1662571
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:06.725954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum146496
Range146496
Interquartile range (IQR)0

Descriptive statistics

Standard deviation236.0164997
Coefficient of variation (CV)115.4835482
Kurtosis207663.2258
Mean2.043724006
Median Absolute Deviation (MAD)0
Skewness412.3919004
Sum3449520
Variance55703.78813
MonotonicityNot monotonic
2022-10-23T20:25:06.775372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01662571
98.5%
13917
 
0.2%
22187
 
0.1%
41294
 
0.1%
31217
 
0.1%
51077
 
0.1%
6836
 
< 0.1%
12763
 
< 0.1%
10744
 
< 0.1%
8625
 
< 0.1%
Other values (816)12629
 
0.7%
ValueCountFrequency (%)
01662571
98.5%
13917
 
0.2%
22187
 
0.1%
31217
 
0.1%
41294
 
0.1%
51077
 
0.1%
6836
 
< 0.1%
7488
 
< 0.1%
8625
 
< 0.1%
9401
 
< 0.1%
ValueCountFrequency (%)
1464961
< 0.1%
1376251
< 0.1%
987761
< 0.1%
876891
< 0.1%
836001
< 0.1%
740841
< 0.1%
611441
< 0.1%
591361
< 0.1%
364521
< 0.1%
343681
< 0.1%

perf_6_month_avg
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct102
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-6.872058838
Minimum-99
Maximum1
Zeros39013
Zeros (%)2.3%
Negative129478
Negative (%)7.7%
Memory size12.9 MiB
2022-10-23T20:25:06.828243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q10.63
median0.82
Q30.97
95-th percentile1
Maximum1
Range100
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation26.55635677
Coefficient of variation (CV)-3.864396013
Kurtosis8.117395115
Mean-6.872058838
Median Absolute Deviation (MAD)0.15
Skewness-3.180621807
Sum-11599073.23
Variance705.2400851
MonotonicityNot monotonic
2022-10-23T20:25:06.878016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99143757
 
8.5%
1132329
 
7.8%
-99129478
 
7.7%
0.73106468
 
6.3%
0.9883611
 
5.0%
0.9762531
 
3.7%
0.7846262
 
2.7%
0.9544152
 
2.6%
0.9639850
 
2.4%
0.8239591
 
2.3%
Other values (92)859831
50.9%
ValueCountFrequency (%)
-99129478
7.7%
039013
 
2.3%
0.01572
 
< 0.1%
0.021053
 
0.1%
0.03703
 
< 0.1%
0.04652
 
< 0.1%
0.051218
 
0.1%
0.061145
 
0.1%
0.072303
 
0.1%
0.081674
 
0.1%
ValueCountFrequency (%)
1132329
7.8%
0.99143757
8.5%
0.9883611
5.0%
0.9762531
3.7%
0.9639850
 
2.4%
0.9544152
 
2.6%
0.9436733
 
2.2%
0.9334815
 
2.1%
0.9222360
 
1.3%
0.9131151
 
1.8%

perf_12_month_avg
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct102
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-6.437946743
Minimum-99
Maximum1
Zeros32975
Zeros (%)2.0%
Negative122050
Negative (%)7.2%
Memory size12.9 MiB
2022-10-23T20:25:06.927251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q10.66
median0.81
Q30.95
95-th percentile0.99
Maximum1
Range100
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation25.84333121
Coefficient of variation (CV)-4.014219477
Kurtosis8.905503219
Mean-6.437946743
Median Absolute Deviation (MAD)0.15
Skewness-3.302181248
Sum-10866352.79
Variance667.8777679
MonotonicityNot monotonic
2022-10-23T20:25:06.977122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99132425
 
7.8%
-99122050
 
7.2%
0.78117662
 
7.0%
0.9892825
 
5.5%
0.9766029
 
3.9%
0.9663441
 
3.8%
0.6653184
 
3.2%
0.947174
 
2.8%
0.9545728
 
2.7%
143675
 
2.6%
Other values (92)903667
53.5%
ValueCountFrequency (%)
-99122050
7.2%
032975
 
2.0%
0.012458
 
0.1%
0.02420
 
< 0.1%
0.03563
 
< 0.1%
0.041026
 
0.1%
0.05646
 
< 0.1%
0.06817
 
< 0.1%
0.071141
 
0.1%
0.081496
 
0.1%
ValueCountFrequency (%)
143675
 
2.6%
0.99132425
7.8%
0.9892825
5.5%
0.9766029
3.9%
0.9663441
3.8%
0.9545728
 
2.7%
0.9438752
 
2.3%
0.9331870
 
1.9%
0.9230160
 
1.8%
0.9129820
 
1.8%

local_bo_qty
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct654
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.6264506535
Minimum0
Maximum12530
Zeros1664518
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2022-10-23T20:25:07.026413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12530
Range12530
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33.72224156
Coefficient of variation (CV)53.83064312
Kurtosis38154.95546
Mean0.6264506535
Median Absolute Deviation (MAD)0
Skewness165.1905479
Sum1057361
Variance1137.189576
MonotonicityNot monotonic
2022-10-23T20:25:07.077102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01664518
98.6%
17151
 
0.4%
22982
 
0.2%
31716
 
0.1%
41224
 
0.1%
5979
 
0.1%
6686
 
< 0.1%
7552
 
< 0.1%
10504
 
< 0.1%
8444
 
< 0.1%
Other values (644)7104
 
0.4%
ValueCountFrequency (%)
01664518
98.6%
17151
 
0.4%
22982
 
0.2%
31716
 
0.1%
41224
 
0.1%
5979
 
0.1%
6686
 
< 0.1%
7552
 
< 0.1%
8444
 
< 0.1%
9348
 
< 0.1%
ValueCountFrequency (%)
125301
< 0.1%
100451
< 0.1%
100241
< 0.1%
86001
< 0.1%
78121
< 0.1%
70481
< 0.1%
70001
< 0.1%
69651
< 0.1%
69551
< 0.1%
67321
< 0.1%

deck_risk
Boolean

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1300377 
True
387483 
(Missing)
 
1
ValueCountFrequency (%)
False1300377
77.0%
True387483
 
23.0%
(Missing)1
 
< 0.1%
2022-10-23T20:25:07.124652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1687615 
True
 
245
(Missing)
 
1
ValueCountFrequency (%)
False1687615
> 99.9%
True245
 
< 0.1%
(Missing)1
 
< 0.1%
2022-10-23T20:25:07.160425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

ppap_risk
Boolean

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1484026 
True
203834 
(Missing)
 
1
ValueCountFrequency (%)
False1484026
87.9%
True203834
 
12.1%
(Missing)1
 
< 0.1%
2022-10-23T20:25:07.196132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
True
1626774 
False
 
61086
(Missing)
 
1
ValueCountFrequency (%)
True1626774
96.4%
False61086
 
3.6%
(Missing)1
 
< 0.1%
2022-10-23T20:25:07.232608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

rev_stop
Boolean

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1687129 
True
 
731
(Missing)
 
1
ValueCountFrequency (%)
False1687129
> 99.9%
True731
 
< 0.1%
(Missing)1
 
< 0.1%
2022-10-23T20:25:07.268391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size3.2 MiB
False
1676567 
True
 
11293
(Missing)
 
1
ValueCountFrequency (%)
False1676567
99.3%
True11293
 
0.7%
(Missing)1
 
< 0.1%
2022-10-23T20:25:07.303588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Interactions

2022-10-23T20:24:49.981566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:04.507222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:07.948352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:11.207675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:14.487880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:17.697890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:20.981875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:24.185362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:27.344307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:30.621472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:33.805243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:37.116009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:40.318353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:43.590037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:46.744158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:50.182020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:04.792815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:08.164536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:11.411321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:14.689371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:17.894444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:21.181375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:24.383108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:27.540233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:30.818838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:34.002725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:37.314203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:40.516297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:43.792767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:46.946380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:50.395076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:05.018389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:08.373652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:11.626765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:14.902932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:18.106278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:21.397154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:24.595780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:27.848001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:31.028590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:34.215676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:37.527026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:40.728783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:44.006084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:47.156232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:50.600819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:05.232001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:08.591546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:11.831702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:15.111768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:18.318096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:21.613384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:24.800848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:28.062027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:31.241991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:34.431003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:37.740872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:40.936087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:44.207147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:47.358203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:50.814399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:05.451979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:08.811979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:12.047423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:15.330530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:18.530257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:21.831704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:25.014271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:28.275397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:31.468881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:34.645884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:37.955786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:41.151317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:44.420175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:47.570025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:51.025535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:05.666875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:09.030105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:12.261648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:15.546086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:18.741706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:22.043775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:25.225193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:28.487185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:31.682942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:34.861128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:38.172266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:41.363562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:44.636274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:47.780100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:51.237652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:05.884048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:09.250084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:12.477082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:15.766797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:18.957380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:22.258589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:25.437442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:28.699842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:31.898497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:35.176774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:38.387691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:41.580207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:44.849672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:47.991712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:51.451999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:06.211706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:09.470199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:12.690051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:15.983088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:22.476673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:25.657398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:28.910893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:32.115138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:38.604795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:41.796152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:45.061366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:48.203724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:51.664414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:06.428604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:09.691403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:12.904075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:16.197737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:19.394185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:32.323342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:38.818686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:42.008411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:45.271318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:51.875696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:06.646985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:09.906697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:52.086834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:06.865217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:36.035384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:39.245308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:45.692922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:39.458930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:42.740386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:49.044929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:52.513543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:07.300014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:10.560660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:13.853853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:29.980669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:33.171980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:36.467394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:39.675399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:42.953161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:33.383487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:27.130248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-23T20:24:36.895233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:40.100132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:43.378044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:46.533424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-23T20:24:49.771281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-23T20:25:07.344085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-23T20:25:07.437091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-23T20:25:07.517087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-23T20:25:07.598715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-23T20:25:07.674226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-23T20:25:07.738284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-23T20:24:53.878918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-23T20:24:56.819129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-23T20:25:02.568900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-23T20:25:03.853999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

skunational_invlead_timein_transit_qtyforecast_3_monthforecast_6_monthforecast_9_monthsales_1_monthsales_3_monthsales_6_monthsales_9_monthmin_bankpotential_issuepieces_past_dueperf_6_month_avgperf_12_month_avglocal_bo_qtydeck_riskoe_constraintppap_riskstop_auto_buyrev_stopwent_on_backorder
010268270.0NaN0.00.00.00.00.00.00.00.00.0No0.0-99.00-99.000.0NoNoNoYesNoNo
110433842.09.00.00.00.00.00.00.00.00.00.0No0.00.990.990.0NoNoNoYesNoNo
210436962.0NaN0.00.00.00.00.00.00.00.00.0No0.0-99.00-99.000.0YesNoNoYesNoNo
310438527.08.00.00.00.00.00.00.00.00.01.0No0.00.100.130.0NoNoNoYesNoNo
410440488.0NaN0.00.00.00.00.00.00.04.02.0No0.0-99.00-99.000.0YesNoNoYesNoNo
5104419813.08.00.00.00.00.00.00.00.00.00.0No0.00.820.870.0NoNoNoYesNoNo
610446431095.0NaN0.00.00.00.00.00.00.00.04.0No0.0-99.00-99.000.0YesNoNoYesNoNo
710450986.02.00.00.00.00.00.00.00.00.00.0No0.00.000.000.0YesNoYesYesNoNo
81045815140.0NaN0.015.0114.0152.00.00.00.00.00.0No0.0-99.00-99.000.0NoNoNoYesNoNo
910458674.08.00.00.00.00.00.00.00.00.00.0No0.00.820.870.0NoNoNoYesNoNo

Last rows

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16878511373539-6.09.036.0130.0130.0130.00.00.054.057.03.0No0.00.030.1042.0NoNoNoYesNoNo
168785214786832.08.00.0966.0966.01116.047.0512.01361.02060.0455.0No0.00.840.7746.0NoNoNoYesNoNo
168785314899200.02.00.02071.03025.03412.04.0764.0764.0765.0657.0No0.00.980.994.0NoNoNoNoNoYes
16878541392420124.08.0140.0410.0780.01240.0128.0464.0849.01074.0111.0No0.00.850.901.0NoNoNoYesNoNo
168785514077540.02.00.010.010.010.00.05.07.07.00.0No0.00.690.695.0YesNoNoYesNoNo
16878561373987-1.0NaN0.05.07.09.01.03.03.08.00.0No0.0-99.00-99.001.0NoNoNoYesNoNo
16878571524346-1.09.00.07.09.011.00.08.011.012.00.0No0.00.860.841.0YesNoNoNoNoYes
1687858143956362.09.016.039.087.0126.035.063.0153.0205.012.0No0.00.860.846.0NoNoNoYesNoNo
1687859150200919.04.00.00.00.00.02.07.012.020.01.0No0.00.730.781.0NoNoNoYesNoNo
1687860(1687860 rows)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

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124000.0NaN0.00.00.00.00.00.00.00.00.0No0.0-99.00-99.000.0NoNoNoYesNoNo6921
277982.012.00.00.00.00.00.00.00.00.00.0No0.00.780.780.0NoNoNoYesNoNo3140
216992.04.00.00.00.00.00.00.00.00.00.0No0.00.730.780.0NoNoNoYesNoNo2690
9155913.012.00.00.00.00.00.00.00.00.01.0No0.00.480.480.0YesNoNoYesNoNo2683
277572.012.00.00.00.00.00.00.00.00.00.0No0.00.630.720.0NoNoNoYesNoNo2534
8025410.012.00.00.00.00.00.00.00.00.01.0No0.00.480.480.0YesNoNoYesNoNo2253
375043.012.00.00.00.00.00.00.00.00.00.0No0.00.630.720.0NoNoNoYesNoNo2169
277782.012.00.00.00.00.00.00.00.00.00.0No0.00.730.790.0NoNoNoYesNoNo2163
9156213.012.00.00.00.00.00.00.00.00.01.0No0.00.580.580.0YesNoNoYesNoNo2137
266932.09.00.00.00.00.00.00.00.00.00.0No0.00.700.660.0NoNoNoYesNoNo2082